To balance the tradeoff between accuracy and efficiency, the feature extraction module consists of one convolutional layer and two bottlenecks. A convolutional layer with a kernel size of \(3\times 1\)  is used to extract low-level information and execute the feature aggregation. Then, the bottleneck is utilized to learn high-level features by using involution with fewer parameters compared to convolution. As shown in Fig. REF  (b), the bottleneck is constructed by replacing the \(3\times 3\)  convolutional layer in the original ResNet [1]} with involution and retaining all the \(1\times 1\)  convolution for channel projection and fusion [2]}. Instead of using a constant channel number as in [1]}, a pyramid architecture with increasing channel numbers is utilized in the Invo-ResNet. Therefore, maxpooling is adopted to decrease the shape of the feature map in order to reduce the computation cost, as shown in Fig. REF (c).
